NEURAL_NET :: ARCHITECTURE ZOO

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AI Architectures & Zoo

Neural Model Zoo & Physics-Informed Architectures

State-of-the-art deep learning architectures specifically engineered for non-linear dynamical systems, causal feature selection, and thermodynamic energy balance preservation.

Transformer
3.2M params

Temporal Fusion Transformer (TFT)

State-of-the-art multi-horizon attention architecture incorporating Variable Selection Networks (VSNs), Gated Residual Networks (GRNs), and static metadata enrichment.

ROC-AUC Score0.984
Lead Accuracy94.2% (±1.5 mo)
Key Architectural Strengths
✓ Variable Selection Network for pruning noisy input drivers✓ Gated Residual Network with ELU activation and layer normalization✓ Interpretable Multi-Head Self-Attention over temporal sequences✓ Quantile regression output heads (0.1, 0.5, 0.9) for uncertainty bounds
Transformer
4.8M params

Informer (ProbSparse Attention)

Efficient long-sequence time-series forecasting transformer utilizing ProbSparse self-attention and generative decoder distilling to achieve O(L log L) time and space complexity.

ROC-AUC Score0.971
Lead Accuracy91.8% (±2.1 mo)
Key Architectural Strengths
✓ ProbSparse self-attention mechanism filtering inactive queries✓ Self-attention distilling operations halving cascading layer length✓ Generative inference decoder predicting long horizons in a single forward pass
Transformer
2.1M params

Time-Series Transformer (Vanilla + CLS)

Standard encoder-only transformer adapted for continuous geospatial time series with learnable sinusoidal positional encodings and a dedicated classification token head.

ROC-AUC Score0.965
Lead Accuracy89.5% (±2.4 mo)
Key Architectural Strengths
✓ Pre-LayerNorm multi-head self-attention encoder stack✓ Learnable temporal embedding combined with feature projection✓ Additive attention pooling over sequence tokens for classification
GNN
1.8M params

Climate Spatial GNN (GCN / SAGE)

Graph Neural Network capturing complex teleconnections and spatial moisture/heat transport across global climate grid cells via message passing over k-NN correlation graphs.

ROC-AUC Score0.978
Lead Accuracy92.7% (±1.8 mo)
Key Architectural Strengths
✓ Dynamic k-NN spatial adjacency graph construction from lat/lon grids✓ GraphSAGE inductive neighborhood aggregation with mean/max pooling✓ Global attention readout layer mapping graph states to tipping risk
GNN
2.5M params

Dynamic Temporal GNN (T-GCN)

Hybrid spatial-temporal neural architecture coupling Graph Convolutional Networks with Gated Recurrent Units (GRUs) to model evolving climate network topology over time.

ROC-AUC Score0.981
Lead Accuracy93.5% (±1.6 mo)
Key Architectural Strengths
✓ Spatial Graph Convolution evaluating localized grid cell interactions✓ Temporal GRU cell tracking historical state transitions across time steps✓ End-to-end joint optimization for spatio-temporal early warning signals
Physics & Causal
950K params

Physics-Informed NN (PINN)

Deep neural network embedding thermodynamic Navier-Stokes and energy balance differential equations directly into the loss function via automatic differentiation residuals.

ROC-AUC Score0.989
Lead Accuracy96.8% (±1.1 mo)
Key Architectural Strengths
✓ Multi-layer perceptron with sinusoidal activation functions✓ PDE residual loss term penalizing violations of conservation of mass/energy✓ Physics-constrained boundary conditions ensuring physical realism
Physics & Causal
N/A (Algorithmic) params

PCMCI Non-Linear Causal Discovery

Tigramite-powered causal discovery algorithm utilizing condition-selection PC algorithm followed by Moment-Conditioned Conditional Independence (MCI) testing.

ROC-AUC Score0.952
Lead Accuracy88.0% (±2.8 mo)
Key Architectural Strengths
✓ PC1 phase removing spurious autocorrelation correlations✓ MCI phase testing conditional independence on parent Markov sets✓ Constructs empirical causal graphs with time-lagged directional arrows
Baseline
2 params

AR(1) Critical Slowing Down Baseline

Classical statistical mechanics indicator measuring the rise in Lag-1 Autocorrelation and rolling variance as a dynamical system approaches a fold bifurcation point.

ROC-AUC Score0.865
Lead Accuracy74.5% (±4.2 mo)
Key Architectural Strengths
✓ Rolling window OLS estimation of AR(1) coefficient phi✓ Kendall tau rank correlation trend significance testing✓ Zero look-ahead empirical variance tracking